






"""simple docstring"""





from __future__ import annotations

from collections import Counter
from random import random






class 	a		:






    def __init__(			self	:				List[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		{}






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				str   )	->						None:
      lowerCamelCase_       	=		{}






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				float   )	->						None:
      if nodea not in self.connections:
        self.add_node(__SCREAMING_SNAKE_CASE   )
      if nodea not in self.connections:
        self.add_node(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		probability






    def   UpperCamelCase			(			self	:				List[Any]   )	->						list[str]:
      return list(self.connections   )






    def   UpperCamelCase			(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				str   )	->						str:
      lowerCamelCase_       	=		0
      lowerCamelCase_       	=		random()

      for dest in self.connections[node]:
        current_probability += self.connections[node][dest]
        if current_probability > random_value:
          return dest
      return ""







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[tuple[str, str, float]]						,							_lowerCamelCase			:						int					) ->							dict[str, int]:
  lowerCamelCase_       	=		MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)

  lowerCamelCase_       	=		Counter(graph.get_nodes()					)
  lowerCamelCase_       	=		start

  for _ in range(_lowerCamelCase					):
    lowerCamelCase_       	=		graph.transition(_lowerCamelCase					)
    visited[node] += 1

  return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()












"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




